Senior Machine Learning Scientist - Agentic Merch

Optimize cost, efficiency, and scalability of AI models, leveraging parameter-efficient fine-tuning (LoRA, QLoRA), knowledge distillation, and hybrid ML approaches. *

Conduct exploratory data analysis on large, noisy retail datasets to uncover patterns, edge cases, and opportunities for model improvement. *

Own end-to-end ML projects from problem formulation and experimentation through production deployment, monitoring, and iteration. *

Define and track success metrics for product grouping quality, product curation success, assortment gap identification, using data to continuously improve system performance. *

Collaborate with top AI research and industry leaders (e.g., Google, Anthropic, Snorkel AI) to explore cutting-edge techniques in LLMs, data labeling automation, and scalable ML workflows. *

Develop agentic AI workflows for automated schema definition, dataset generation, production relationship modeling, and LLM-based judgment systems to validate catalog data. *

Mentor junior ML scientists and contribute to a culture of technical rigor, collaboration, and knowledge sharing. *

Partner closely with product managers, supplier experience teams, and engineers to translate business needs into scalable ML solutions that integrate into production workflows.

Minimum 5+ years of experience with PhD, or 9+ years of industry experience with MS, or 10+ years of experience with a BS in a quantitative STEM field. *

2+ years of experience working as a Tech Lead, preferably developing agentic AI workflows at scale. *

Strong proficiency in Python and the ML ecosystem, with hands-on experience using frameworks such as PyTorch, XGBoost, or similar. *

Proven experience deploying ML models into production, including collaboration with engineering partners and operating models at scale. *

Deep expertise in building and scaling graph relationships based on structured and unstructured data and agentic workflows to uncover customer demand, organize products to demand spaces, and drive automated gap identification. *

Hands-on experience with representation learning, embedding-based similarity search, and graph-based ML to construct demand huddles and model product substitutability across multimodal data (text, images, attributes). *

Demonstrated ability to design and deploy agentic AI workflows and LLM-based judgment systems for scalable curation, schema definition, and automated catalog validation. *

Experience working with large, complex datasets and scalable data pipelines, balancing model accuracy, latency, and operational cost efficiency. *

Strong written and verbal communication skills, with the ability to clearly explain technical concepts and influence cross-functional merchant, product, and engineering partners.

Nice to Have

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Experience with large-scale ecommerce or catalog data, including product attributes, taxonomy, text, and images. *

Familiarity with embedding-based similarity search, approximate nearest neighbors, or graph-based ML techniques. *

Experience with MLOps tooling (feature stores, MLflow, monitoring) and orchestration frameworks (Airflow, Kubeflow). *

Familiarity with cloud platforms (GCP, AWS, or Azure) and containerization tools (Docker). *

Exposure to LLMs or multimodal models for product understanding or data enrichment.